The medical community, and the research community supporting the medical industry, strive to improve methods to assess and respond in a more timely manner to abnormalities identified by various measurement techniques. Often, seconds can mean the difference between life and death. Techniques used to measure abnormal functions in patients include, for example, ECGs, EMGs, EOGs, EEGs, and others. ECG measurements record heart function. EMG measurements record physiological properties of the muscles. EOG measurements record retinal data. EEG measurements record brain activity. Common among these analytical tools is the use of a means to record electrical data related to the function in question from the target organ or body system. Inherent in the measurement systems currently in use, however, are various drawbacks, including artifacts that mask or clutter recorded impulses or signals. Due to these and other drawbacks, complicated and cumbersome calculations are used to try and recover an accurate signal. These calculations are time consuming, causing a lapse between gathering the measured and recorded data and assessing malfunctions or abnormalities that are shown. This time lapse may be the difference between life and death for patients experiencing a critical episode.
Attempts have been made to address some of these drawbacks. For example, various non invasive measurements have been developed over the last few years to assess brain activity, and simultaneous recording of EEG-fMRI is one of them that is fast emerging as a tool in research and clinical studies related to neurophysiology. EEG signals reflect synchronous neuronal activity of the brain with a high temporal resolution in the order of milliseconds while fMRI measures the neural correlates using indirect means such as changes in blood oxygenation levels and has a very high spatial resolution (0.5-2 mm). These two types of data provide information complementary to each other Combined EEG-fMRI techniques are used to identify important spontaneous EEG activities, such as epileptic seizures, interictal spikes, the alpha rhythm, and sleep waves. It is also vital in identifying symptoms that change over short periods of time.
However, recording the EEG signal within the strong magnetic field of the MR scanner introduces two main types of artifacts: (i) one is due to the rapidly changing magnetic field or gradient known as the radiofrequency (RF) artifact; and (ii) the second is due to the small and tiny movements of the electrodes on the scalp because of the pulsatile changes in the blood flow coupled to the cardiac motion of the patient known as the ballistocardiogram (BCG). The BCG artifact is embedded within the EEG signal. It is highly non stationary in nature and varies slightly in shape and amplitude on a beat by beat basis, making it difficult to identify and remove. Moreover, it shares spectral components with the alpha and mu rhythm bands of the EEG signals. The elimination of the BCG artifacts is therefore particularly important in identification and study of various neurophysiologic disorders such as epileptic spikes, discharges and others.
The current methods for BCG artifact removal include Average Artifact Subtraction (AAS), Adaptive Filtering, and Independent Component Analysis (ICA). The most commonly used amongst them is the AAS method, wherein the average of the last 10 heartbeats is computed and this averaged waveform is subtracted from the original signal to give a clean record of the EEG signal. This was discussed in Allen, P. J., Pollizi, G., Krakow, K., Fish, D. R. and Lemieux, L. (1998), “Identification of EEG events in the MR scanner: The problem of pulse artifact and a method for its subtraction”. Neuroimage 8, 229-239. However, this method relies heavily on the assumption of stationarity. The Adaptive Filtering methods, disclosed in Bonmassar, G., Purdon, P. L., Jaaskelainen, I. P., Chiappa, K., Solo, V., Brown, E. N. and Belliveau, J. W. (2002). “Motion and ballistocardiogram artifact removal for interleaved recording of EEG and Ep's during MRI”. Neurolmage 16, 1127-1141, make use of a reference signal which is generally not commonly available in EEG-fMRI measurements. Additionally, this reference signal when acquired near the scalp does contain artifacts and cannot be treated as a standard reference signal. ICA techniques are shown to be successful in eliminating the BCG artifact but are computationally rigorous and exhaustive, making it difficult to implement in real time. See Srivastava G., Crottaz-Herbefte S., Lau K. M., Glover, G. H., and Menon, V., “ICA Based Procedures for Removing Ballistocardiogram Artifacts From EEG Data Acquired in the MRI Scanner”, NeuroImage 24, pp. 50-66 (2004). Furthermore it relies on the assumption that the BCG artifact in each channel is a linear mixture of the underlying BCG sources, which is not necessarily true. There is a need for a mechanism which is capable of removing artifacts and promoting real-time assessment of recorded abnormalities.
Another example of a measurement technique that suffers from a lack of real-time assessment is in the area of heart health. Heart attacks and other ischemic events of the heart are among the leading causes of death and disability in the United States. In general, the susceptibility of a particular patient to heart attack or the like can be assessed by examining the heart for evidence of ischemia (insufficient blood flow to the heart tissue itself resulting in an insufficient oxygen supply) during periods of elevated heart activity. Of course, it is highly desirable that the measuring technique be sufficiently benign to be carried out without undue stress to the heart (the condition of which might not yet be known) and without undue discomfort to the patient.
The cardiovascular system responds to changes in physiological stress by adjusting the heart rate, which adjustments can be evaluated by measuring the surface ECG R-R intervals. The time intervals between consecutive R waves indicate the intervals between the consecutive heartbeats (RR intervals). This adjustment normally occurs along with corresponding changes in the duration of the ECG QT intervals, which characterize the duration of electrical excitation of cardiac muscle and represent the action potential duration averaged over a certain volume of cardiac muscle. Generally speaking, an average action potential duration measured as the QT interval at each ECG lead may be considered as an indicator of cardiac systolic activity varying in time.
Work has been done in the use of Hermite polynomials and neuro-fuzzy network used to recognize online heartbeat. See “On-line Heartbeat Recognition Using Hermite Polynomials and Neuro-fuzzy Network,” by T. H. Linh, et al, IEEE Transactions on Instrumentation and Measurement. This work, however, is based on an analog technique which is not suitable for real-time analysis. The continuous functions used by Linh, et al are only orthogonal over an infinite domain, meaning that on a practical finite interval they are no longer orthogonal. Therefore, the results have an error in representing any real signal. Further, Linh, et al's formula for the coefficients is computationally intensive, involving the use of the “Singular Value Decomposition (SVD) and pseudo-inverse technique.” That intensive computation results in Linh et al. using at most 15 coefficients in any given expansion, as computation of more coefficients would be time consuming using that method for the continuous case.
As is noted above, ischemic heart disease is a common cause of death and disability in industrialized countries. The ECG is one of the most important tools for the diagnosis of ischemia. Long term continuous ECG monitoring is found to offer more prognostic information than the standard 12 lead ECG, concerning ischemia. Given the usefulness of ECG in identifying ischemia, there is a need in the art for a reliable computer based method to interpret ECG results in order to identify the abnormalities associated with not only ischemia, but other types of heart disease as well.
What is lacking is a mechanism for accurately assessing measured functions, with the removal of artifacts and/or noise, in real-time, to assist in patient diagnosis and treatment. No assessment technique is currently available for the real-time evaluation of digital data, that is equally applicable to signals collected from a wide variety of measurement instruments used in a variety of biomedical disciplines, and that is capable of identifying and isolating a notable signal from an overall signal distorted by other factors, such as noise, artifacts, etc. Such a technique and method would enhance the capability of medical professionals relying on digital, electrical data to diagnose immediately the source of acute conditions in patients, not to mention the benefits to long-term treatment based on comparative data and assessment. The technique provided herein, based on the application of discrete Hermite functions to such digital data, provides the needed real-time assessment that is lacking in currently available systems.